Method and apparatus for facilitating persona-based agent interactions with online visitors

ABSTRACT

A method and apparatus for facilitating persona-based agent interactions with online visitors is disclosed. A plurality of persona related attributes is extracted from a textual transcript of each interaction between an agent of an enterprise and an online visitor. A feature vector data representation is generated based on the plurality of persona related attributes extracted from each interaction to configure a plurality of feature vector data representations. The plurality of feature vector data representations is classified based on a plurality of persona-based clusters, which enables classification of the plurality of online visitors into the plurality of persona-based clusters. A learning model is trained for each persona-based cluster using utterances of online visitors classified into a respective persona-based cluster. The learning model is trained to mimic a visitor persona representative of the respective persona-based cluster. The trained learning model is configured to facilitate the persona-based agent interactions with the online visitors.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to provisional patent application No.201841036381 filed in India on Sep. 26, 2018, which is incorporatedherein in its entirety by this reference thereto.

TECHNICAL FIELD

The present technology generally relates to solutions facilitatinginteractions between online visitors and agents of an enterprise and,more particularly, to a method and apparatus for facilitatingpersona-based agent interactions with the online visitors to improvevisitor interaction experiences.

BACKGROUND

Online enterprise channels, such as the enterprise Website andenterprise social media portals, display enterprise products and/orservices and routinely attract many visitors. Existing or potentialcustomers of the enterprise visiting the online enterprise channels arereferred to herein as online visitors. The enterprises attempt to engagewith the online visitors and, in general, provide an enriched experienceto the online visitors to enhance chances of sale or to improve thelikelihood of the online visitors visiting the online enterprisechannels again.

Currently, an appropriate treatment for an online visitor is determinedbased on the online visitor's attributes. For example, an intention ofthe online visitor to make a purchase on the Website or to click on anadvertisement (also referred to herein as ‘Ad’) during the visitor'scurrent visit to the Website is predicted based on the visitor'sattributes, such as current and past journeys on the Website, deviceused for accessing the Website, current visitor location, and the like.If it is determined that the online visitor will perform the desiredaction, then an appropriate treatment such as an offer to chat with acustomer support representative of an enterprise may be selected andoffered to the online visitor during the ongoing visit to the enterpriseWebsite. Such treatment is provided to the online visitor to influencethe online visitor to take certain desired action, such as for example,to click on an Ad, to engage in a purchase transaction, and the like.

The online visitors who accept the offer to chat with enterprisecustomer support representatives (hereinafter referred to as an ‘agent’)may be associated with different personas and, as such, a standardtreatment for all online visitors may be counterproductive. In anillustrative example, one online visitor may be associated with a‘convenience customer’ persona, implying that the online visitor seeksquick resolution to issues. If such a visitor were to be routed to anagent, who is trained to ask a lot of questions and seek visitorconfirmation at every stage, then the visitor interaction experience mayget ruined on account of the delay in resolving the issue. In anotherillustrative example, one online visitor may be associated with a‘deal-seeker’ persona implying that the online visitor seeks discountsor promotional offers on purchase transactions. Accordingly, if such anonline visitor were to be routed to an agent, who is trained to sellonly low value goods, which are typically not associated with offers ordiscounts, then the visitor interaction experience may get ruined. Insome cases, the online visitor may abandon the interaction, perhapsnever to return.

Accordingly, there is a need to provide improved interaction experienceto the online visitors. Further, it would be advantageous to trainconversational agents to interact with online visitors associated withdifferent personas so as to provide improved interaction experience tothe online visitors.

SUMMARY

In one embodiment, a computer-implemented method for facilitatingpersona-based agent interactions with online visitors is disclosed. Themethod extracts, by a processor, a plurality of persona relatedattributes from a textual transcript of each interaction from among aplurality of interactions between agents of an enterprise and aplurality of online visitors visiting enterprise interaction channels.The plurality of persona related attributes is extracted from eachinteraction in relation to a persona of an online visitor engaged in therespective interaction. The method generates, by the processor, afeature vector data representation based, at least in part, on theplurality of persona related attributes extracted from each interaction.The generation of the feature vector data representation in relation toeach interaction from among the plurality of interactions configures aplurality of feature vector data representations. The method,classifies, by the processor, the plurality of feature vector datarepresentations based on a plurality of persona-based clusters. Theclassification of the plurality of feature vector data representationsbased on the plurality of persona-based clusters enables classificationof the plurality of online visitors into the plurality of persona-basedclusters. For each persona-based cluster from among the plurality ofpersona-based clusters, the method trains, by the processor, a learningmodel using utterances of online visitors classified into a respectivepersona-based cluster. The learning model is trained to mimic a visitorpersona representative of the respective persona-based cluster. Thetrained learning model is configured to facilitate the persona-basedagent interactions.

In another embodiment, an apparatus for facilitating persona-based agentinteractions with online visitors is disclosed. The apparatus includes aprocessor and a memory. The memory stores instructions. The processor isconfigured to execute the instructions and thereby cause the apparatusto extract a plurality of persona related attributes from a textualtranscript of each interaction from among a plurality of interactionsbetween agents of an enterprise and a plurality of online visitorsvisiting enterprise interaction channels. The plurality of personarelated attributes is extracted from each interaction in relation to apersona of an online visitor engaged in the respective interaction. Theapparatus generates a feature vector data representation based, at leastin part, on the plurality of persona related attributes extracted fromeach interaction. The generation of the feature vector datarepresentation in relation to each interaction from among the pluralityof interactions configures a plurality of a feature vector datarepresentations. The apparatus classifies the plurality of a featurevector data representations based on a plurality of persona-basedclusters. The classification of the plurality of a feature vector datarepresentations based on the plurality of persona-based clusters enablesclassification of the plurality of online visitors into the plurality ofpersona-based clusters. For each persona-based cluster from among theplurality of persona-based clusters, the apparatus trains a learningmodel using utterances of online visitors classified into a respectivepersona-based cluster. The learning model is trained to mimic a visitorpersona representative of the respective persona-based cluster. Thetrained learning model is configured to facilitate the persona-basedagent interactions.

In yet another embodiment, a computer-implemented method forfacilitating persona-based agent interactions with online visitors isdisclosed. The method performs, by a processor, for each interactionfrom among a plurality of interactions between agents of an enterpriseand a plurality of online visitors visiting enterprise interactionchannels: (1) extract a plurality of utterances of an online visitorfrom a textual transcript of a respective interaction, and (2) for eachutterance from among the plurality of utterances, perform a predefinedpersonality trait evaluation to extract a plurality of persona relatedattributes. The plurality of persona related attributes is extractedfrom each interaction in relation to a persona of the online visitorengaged in the respective interaction. The method generates, by theprocessor, a feature vector data representation based, at least in part,on the plurality of persona related attributes extracted from eachinteraction. The generation of the feature vector data representation inrelation to each interaction from among the plurality of interactionsconfigures a plurality of feature vector data representations. Themethod classifies, by the processor, the plurality of feature vectordata representations based on a plurality of persona-based clusters. Theclassification of the plurality of feature vector data representationsbased on the plurality of persona-based clusters enables classificationof the plurality of online visitors into the plurality of persona-basedclusters. For each persona-based cluster from among the plurality ofpersona-based clusters, the method trains, by the processor, a RecurrentNeural Network (RNN) model using utterances of online visitorsclassified into a respective persona-based cluster. The RNN model istrained to mimic a visitor persona representative of the respectivepersona-based cluster. The trained learning model is configured tofacilitate the persona-based agent interactions.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a representation of a customer engaged in a chatconversation with an automated conversational agent of an enterprise, inaccordance with an example scenario;

FIG. 2 is a representation showing an apparatus configured to facilitatepersona-based agent interactions with online visitors, in accordancewith an embodiment of the invention;

FIG. 3 is a block diagram of the apparatus of FIG. 2, in accordance withan embodiment of the invention;

FIG. 4 is a simplified representation of a table for illustratingfeature vector data representations generated corresponding to theplurality of visitors, in accordance with an embodiment of theinvention;

FIG. 5 shows a representation of an example process flow forfacilitating persona-based training of learning models, in accordancewith an embodiment of the invention;

FIGS. 6, 7 and 8 depict block diagrams for illustrating an exampletraining of the learning models, in accordance with an embodiment of theinvention;

FIG. 9 shows a block diagram representation for illustrating generationof a sequential output of words configuring the conversational agentresponse in response to a visitor query, in accordance with anembodiment of the invention;

FIG. 10 shows a simplified block-diagram representation of anarchitecture of an RNN model for facilitating persona-based training oflearning models, in accordance with an embodiment of the invention; and

FIG. 11 shows a flow diagram of a method for facilitating persona-basedagent interactions with online visitors, in accordance with anembodiment of the invention.

DETAILED DESCRIPTION

The detailed description provided below in connection with the appendeddrawings is intended as a description of the present examples and is notintended to represent the only forms in which the present example may beconstructed or utilized. However, the same or equivalent functions andsequences may be accomplished by different examples.

FIG. 1 shows a representation 100 of a customer 102 engaged in a chatconversation 104 with an automated conversational agent 106 of anenterprise, in accordance with an example scenario. The term‘enterprise’ as used throughout the description may refer to acorporation, an institution or a small/medium sized company. Forexample, the enterprise may be a banking enterprise, an educationalinstitution, a financial trading enterprise, an aviation company, aconsumer goods enterprise or any such public or private sectorenterprise. It is understood that the enterprise may be associated withpotential and existing users of products, services and/or informationoffered by the enterprise. Such existing or potential users ofenterprise offerings are referred to herein as customers of theenterprise. The representation 100 depicts one example customer of theenterprise as the customer 102 for illustration purposes.

Most enterprises, nowadays, extend dedicated customer support facilityto their customers. A typical customer support center may include anumber of customer service representatives, such as live agents,automated conversational agents and self-assist systems, such as eitherWeb or mobile digital self-service, and/or Interactive Voice Response(IVR) systems. The customer support representatives are trained tointeract with the customers for providing information to the customers,selling to them, answering their queries, addressing their concerns,and/or resolving their issues. The representation 100 depicts oneexample customer support representative associated with the enterpriseas the automated conversational agent 106. The automated conversationalagent 106 may be deployed in a remote customer support center (not shownin FIG. 1). It is noted that for purposes of the description, the liveagents and the automated conversational agents are collectively referredto as ‘conversational agents’ or ‘agents’. Moreover, the automatedconversational agents are also referred to herein as ‘virtual agents’ or‘chatbots’ or simply as ‘bots’.

In an illustrative scenario, the customer 102 may access a website 108using a Web browser application 110 installed on a personal electronicdevice 112 (exemplarily depicted to be a desktop computer). The website108 may be hosted on a remote Web server and the Web browser application110 may be configured to retrieve one or more Web pages associated withthe website 108 from the remote Web server over a communication network(not shown in FIG. 1). An example of the communication network mayinclude the Internet. It is understood that the website 108 may attracta large number of existing and potential customers, such as the customer102. The customer 102, on account of accessing an online enterpriseinteraction channel (i.e. the website 108), is hereinafter referred toas an online visitor 102.

In the representation 100, the website 108 is exemplarily depicted to bean E-commerce website displaying a variety of products and services forsale to online visitors during their journey on the website 108. It isnoted that the term ‘journey’ as used throughout the description refersto a path an online visitor, such as the online visitor 102, may take toreach his/her goal when using a particular interaction channel. Forexample, the online visitor's journey on the website 108 may includeseveral Web page visits and decision points that carry the onlineinteraction of the online visitor 102 from one step to another step.

In an example scenario, an activity of the online visitor 102 on thewebsite 108 during the journey of the online visitor 102 on the website108 may be tracked and the tracked information along with otherinformation, such as past activity on the website 108, previous chatconversations with agents, type of device/browser/OS used for accessingthe website 108, and the like, may be used to determine an intention ofthe online visitor 102. For example, an intention of the online visitor102 to perform a desired action, such as make a purchase transaction onthe website 108 or click on a banner advertisement may be determined. Ifit is determined that the online visitor 102 will perform the desiredaction, then an appropriate treatment such as an offer to chat with anagent of an enterprise or an offer to speak with a customer supportrepresentative like a human agent or the automated conversational agent106 may be selected and provided to the online visitor 102. In anillustrative example, a widget displaying text ‘Need Assistance, Talk toour Agent!!’ may be displayed on the current UI of the website 108. Aselection input on the widget by the online visitor 102 may cause a chatconsole, such as the chat console 120 to pop up, for facilitating thechat conversation 104 between the online visitor 102 and the automatedconversational agent 106.

It is noted that in some example scenarios, the online visitor 102 mayalso call a customer care number displayed on the website 108 andconnect with a conversational agent (such as the live agent or aninteractive voice response (IVR) system) to seek assistance from theconversational agent. It is understood that the conversation may beembodied as voice conversation in such a scenario.

In many example scenarios, the conversational agents may not be trainedto handle online visitors with different personalities (i.e. personas).For example, the automated conversational agent 106, though trained inthe relevant technology/service area may not be trained to handleagitated online visitors. Accordingly, if an agitated visitor were to berouted to the automated conversational agent 106, who is not trained tohandle an agitated visitor, then the response by the automatedconversational agent 106 to the visitor may not soothe or pacify thevisitor's concerns and, in fact, may ruin an interaction experience ofthe online visitor 102. In some cases, the online visitor 102 mayabandon the interaction altogether.

Various embodiments of the present technology provide a method andapparatus that are capable of overcoming these and other obstacles andproviding additional benefits. More specifically, various embodiments ofthe present invention disclose a method and apparatus for training ofconversational agents to enable the conversational agents to handlevisitor interactions involving various visitor personas. Morespecifically, embodiments disclosed herein enable creation of modelsusing deep learning Neural Network (NN) that learns an efficientrepresentation of the previous utterances as a context and uses it insubsequent reply generation. More specifically, the models are trainedto incorporate persona in automated conversational agents (also referredto herein as chatbots). Incorporating persona in automatedconversational agents may be beneficial in two different ways. Anautomated conversational agent mimicking visitors' persona may be usedfor live agent training since different visitors have differentpersonalities and even for similar issues can react differently, whilechatting with live agents. For example, some visitors may be lenient andpatient while some visitors can be highly demanding. Persona-based agenttraining will equip live agents to handle online visitors in anefficient manner. Similarly, a live agent's persona may be incorporatedin an automated conversational agent and such a trained automatedconversational agent may be used for greater compatibility whileconversing with a visitor with known personality traits.

An apparatus for facilitating persona-based agent interactions with theonline visitors is explained with reference to FIG. 2.

FIG. 2 is a representation showing an apparatus 200 configured tofacilitate persona-based agent interactions with online visitors, inaccordance with an embodiment of the invention. The term ‘facilitatingpersona-based agent interactions’ as used herein implies enablingconversational agents, i.e. live agents or automated conversationalagents, to handle interactions with online visitors with differentpersonas. More specifically, the term ‘facilitating persona-based agentinteractions’ as used herein implies providing training toconversational agents to handle different personas of online visitors soas to provide the best possible interaction experience to the onlinevisitors. For example, if an online visitor is aggressive or extremelyangry, then the agent is trained to first soothe or pacify the onlinevisitor and thereafter work towards resolution of the concern of theonline visitor in an expeditious manner. In another illustrativeexample, if an online visitor is disappointed with an enterpriseservice, then the agent is trained to cheer the online visitor, forexample by offering promotional offers or discount coupons, and thelike.

In FIG. 2, the apparatus 200 is exemplarily depicted as a block in therepresentation. Moreover, the apparatus 200 is depicted to be inoperative communication with a plurality of remote entities. In at leastone example embodiment, the apparatus 200 is embodied as an interactionplatform including a set of software layers on top of existing hardwaresystems. The apparatus 200 is configured to connect to a communicationnetwork, such as a network 250. The network 250 may be embodied as awired communication network (for example, Ethernet, local area network(LAN), etc.), a wireless communication network (for example, a cellularnetwork, a wireless LAN, etc.) or a combination thereof (for example,the Internet).

Using the network 250, the apparatus 200 is configured to be inoperative communication with various enterprise interaction channels204. Most enterprises, nowadays, offer various options to its customersto interact with the enterprise. For example, an enterprise may providea website or a Web portal, i.e. a Web channel, to enable the customersto locate products/services of interest, to receive information aboutthe products/services, to make payments, to lodge complaints, and thelike. In another illustrative example, an enterprise may offer automatedconversational agents to interact with the customers and enableself-service. In yet another illustrative example, an enterprise mayoffer dedicated customer sales and service representatives, such as liveagents and automated conversational agents, to interact with thecustomers by engaging in voice conversations, i.e. use a speechinteraction channel, and/or chat conversations, i.e. use a chatinteraction channel. Similarly, the enterprises may offer otherinteraction channels such as an Email channel, a social media channel, anative mobile application channel, and the like.

In the representation shown in FIG. 2, a customer support facility 206including human resources and machine-based resources for facilitatingcustomer interactions, is depicted. More specifically, the customersupport facility 206 is exemplarily depicted to include two live agents208 and 210 (who provide online visitors with chat-based/onlineassistance and voice-based assistance, respectively) and an automatedconversational agent 212 (which may be similar to the automatedconversational agent 106 shown in FIG. 1) capable of offering customerswith IVR/chat-based assistance. It is understood that the customersupport facility 206 may also include other Web or digital self-assistmechanisms. Moreover, it is noted that the customer support facility 206is depicted to include only two live agents 208 and 210 and theautomated conversational agent 212 for illustration purposes and it isunderstood that the customer support facility 206 may include fewer ormore number of resources than those depicted in FIG. 2.

The representation further depicts a plurality of customers, such as acustomer 214, a customer 216 and a customer 218. The term ‘customers’ asused herein includes both existing customers as well as potentialcustomers of information, products and services offered by theenterprise. Moreover, the term ‘customer’ of the enterprise may includeindividuals, groups of individuals, other organizational entities etc.It is understood that three customers are depicted in FIG. 2 for examplepurposes and that the enterprise may be associated with many suchcustomers. In some example scenarios, the customers 214, 216 and 218 maybrowse the Website and/or interact with the resources deployed at thecustomer support facility 206 over the network 250 using theirrespective electronic devices. Examples of such electronic devices mayinclude mobile phones, smartphones, laptops, personal computers, tabletcomputers, personal digital assistants, smart watches, web-enabledwearable devices and the like. The customers, such as the customer 214and 216, which visit online enterprise interaction channels are referredto herein as online visitors.

The apparatus 200 is configured to be in operative communication withthe customer support facility 206 through the network 250. Morespecifically, the apparatus 200 may be in operative communication withdevices of live agents, with automated conversational agents, and/orwith server mechanisms monitoring the electronic devices deployed at thecustomer support facility 206 through the network 250. In at least oneexample embodiment, on account of such operative communication, theapparatus 200 may be configured to track availability of the agent insubstantially real-time. Moreover, in some embodiments, the apparatus200 may also receive transcripts of conversations between theconversational agents and the online visitors in substantiallyreal-time.

The apparatus 200 is further configured to be in operative communicationwith devices of the customers (including the online visitors). Forexample, the apparatus 200 may be configured to be in operativecommunication with the enterprise native mobile applications installedin the devices of the online visitors and also with relatedapplications, such as Virtual Assistants (VAs) deployed in the devicesof the customers.

The apparatus 200 is configured to facilitate persona-based agentinteractions with online visitors. The effecting of persona-based agentinteractions with online visitors is further explained in detail withreference to various components of the apparatus 200 in FIG. 3.

FIG. 3 is a block diagram of the apparatus 200 of FIG. 2, in accordancewith an embodiment of the invention. As explained with reference to FIG.2, the apparatus 200 may be embodied as an interaction platform with oneor more components of the apparatus 200 implemented as a set of softwarelayers on top of existing hardware systems. The interaction platform isconfigured to engage in bi-directional communication with enterpriseinteraction channels and/or data gathering Web servers linked to theenterprise interaction channels over a communication network (such asthe network 250 shown in FIG. 2). For example, the interaction platformmay communicate with the data gathering Web servers to receiveinformation related to online visitor interactions, such as onlinevisitor chat interactions or voice interactions, in an on-going mannerin real-time. Further as explained with reference to FIG. 2, theinteraction platform may also be capable of engaging in operativecommunication with personal devices of the online visitors andconfigured to receive information related to visitor-enterpriseinteractions from the personal devices of the online visitors.

The apparatus 200 includes at least one processor, such as a processor302 and a memory 304. It is noted that although the apparatus 200 isdepicted to include only one processor, the apparatus 200 may includemore number of processors therein. In an embodiment, the memory 304 iscapable of storing machine executable instructions, referred to hereinas platform instructions 305. Further, the processor 302 is capable ofexecuting the platform instructions 305. In an embodiment, the processor302 may be embodied as a multi-core processor, a single core processor,or a combination of one or more multi-core processors and one or moresingle core processors. For example, the processor 302 may be embodiedas one or more of various processing devices, such as a coprocessor, amicroprocessor, a controller, a digital signal processor (DSP), aprocessing circuitry with or without an accompanying DSP, or variousother processing devices including integrated circuits such as, forexample, an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a microcontroller unit (MCU), a hardwareaccelerator, a special-purpose computer chip, or the like. In anembodiment, the processor 302 may be configured to execute hard-codedfunctionality. In an embodiment, the processor 302 is embodied as anexecutor of software instructions, wherein the instructions mayspecifically configure the processor 302 to perform the algorithmsand/or operations described herein when the instructions are executed.

The memory 304 may be embodied as one or more volatile memory devices,one or more non-volatile memory devices, and/or a combination of one ormore volatile memory devices and non-volatile memory devices. Forexample, the memory 304 may be embodied as semiconductor memories (suchas mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flashmemory, RAM (random access memory), etc.), magnetic storage devices(such as hard disk drives, floppy disks, magnetic tapes, etc.), opticalmagnetic storage devices (e.g., magneto-optical disks), CD-ROM (compactdisc read only memory), CD-R (compact disc recordable), CD-R/W (compactdisc rewritable), DVD (Digital Versatile Disc) and BD (BLU-RAY® Disc).

In at least some embodiments, the memory 304 is configured to storelogic and instructions for facilitating conversion of voiceconversations to a textual form. For example, the memory 304 may storeinstructions/logic for Automatic Speech Recognition (ASR) and NaturalLanguage Processing (NLP) techniques using special grammar (i.e. domainvocabulary) to facilitate textual transcription of voice conversations.

The memory 304 also stores instructions related to Recurrent NeuralNetwork (RNN) models capable of facilitating RNN based encoding anddecoding of utterances associated with the interactions. Somenon-limiting examples of such RNN models include, but are not limitedto, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) andBi-directional RNN. It is noted that an RNN model trained using encodinglogic, in effect, configures an RNN based encoder, whereas an RNN modeltrained using decoding logic, in effect, configures an RNN baseddecoder.

The memory 304 further stores at least one clustering algorithm fromamong K-means algorithm, a self-organizing map (SOM) based algorithm, aself-organizing feature map (SOFM) based algorithm, a density-basedspatial clustering algorithm, an optics clustering based algorithm andthe like, for facilitating clustering of feature vector datarepresentations as will be explained in further detail later.Furthermore, the memory 304 may also store instructions for computingsimilarity or dissimilarity between vector representations. For example,the memory 304 may store instructions related to computation ofdissimilarity measures such as optimal matching, longest commonsubsequence, longest common prefix, hamming distance, and the like.

The memory 304 may also be configured to store text mining and intentionprediction models as classifiers. Some examples of classifiers includemodels based on Logistic Regression (LR), Artificial Neural Network(ANN), Support Vector Machine (SVM) with Platt scaling, and the like.The classifiers may be used to predict intention of each online visitorfor requesting an interaction with the agent.

In at least some embodiments, the memory 304 may include a database (notshown in FIG. 3) configured to store raw data related to interactionsbetween the agents and the visitors. The database may also store textualtranscripts corresponding to the stored interactions. Further, thedatabase may store information related to workflows extracted frominteractions and the workflow groups associated with interactions, whichare clustered or categorized based on similarity in associatedworkflows.

The apparatus 200 also includes an input/output module 306 (hereinafterreferred to as an ‘I/O module 306’) and at least one communicationmodule such as a communication module 308. The I/O module 306 includesmechanisms configured to receive inputs from and provide outputs to theuser of the apparatus 200. The term ‘user of the apparatus 200’ as usedherein refers to any individual or groups of individuals assigned withoperating the apparatus 200 for facilitating persona-based agentinteractions with online visitors. In an illustrative example, anenterprise may employ several data scientists, Machine Learning (ML)and/or Artificial Intelligence (AI) analysts, Information Technology(IT) professionals, scientists and researchers for configuring andoperating the apparatus 200 embodied as an interaction platform. In anillustrative example, the I/O module 306 may enable the user of theapparatus 200 to define various workflow stages to facilitatepersona-based agent interactions with online visitors. In anotherillustrative example, the I/O module 306 may enable the user of theapparatus 200 to feed/input information related to agents, such as agentdomain specialization for instance, to enable routing of interactionrequests from online visitors to appropriate agents within a customersupport facility. To provide such inputs and view corresponding outputs,the I/O module 306 may include at least one input interface and/or atleast one output interface. Examples of the input interface may include,but are not limited to, a keyboard, a mouse, a joystick, a keypad, atouch screen, soft keys, a microphone, and the like. Examples of theoutput interface may include, but are not limited to, a display such asa light emitting diode display, a thin-film transistor (TFT) display, aliquid crystal display, an active-matrix organic light-emitting diode(AMOLED) display, a microphone, a speaker, a ringer, a vibrator, and thelike.

In an example embodiment, the processor 302 may include I/O circuitryconfigured to control at least some functions of one or more elements ofthe I/O module 306, such as, for example, a speaker, a microphone, adisplay, and/or the like. The processor 302 and/or the I/O circuitry maybe configured to control one or more functions of the one or moreelements of the I/O module 306 through computer program instructions,for example, software and/or firmware, stored on a memory, for example,the memory 304, and/or the like, accessible to the processor 302.

The communication module 308 is configured to facilitate communicationbetween the apparatus 200 and one or more remote entities over acommunication network, such as the network 250 explained with referenceto FIG. 2. For example, the communication module 308 may enablecommunication between the apparatus 200 and customer support facilities,such as the customer support facility 206 shown in FIG. 2. In anillustrative example, the communication module 308 receives informationrelated to voice or chat interactions between online visitors andconversational agents being conducted using various interactionchannels, in real-time and provides the information to the processor302.

To that effect, the communication module 308 may include several channelinterfaces to receive information from a plurality of enterpriseinteraction channels. In at least some embodiments, the communicationmodule 308 may include relevant Application Programming Interfaces(APIs) to communicate with remote data gathering servers associated withsuch enterprise interaction channels over the network 250. Each channelinterface may further be associated with a respective communicationcircuitry such as for example, a transceiver circuitry including antennaand other communication media interfaces to connect to the network 250.The communication circuitry associated with each channel interface may,in at least some example embodiments, enable transmission of datasignals and/or reception of signals from remote network entities, suchas Web servers hosting enterprise Website or a server at a customersupport facility configured to maintain real-time information related tointeractions between online visitors and conversational agents.

In some embodiments, the information may also be collated from theplurality of devices utilized by the online visitors. To that effect,the communication module 308 may be in operative communication withvarious online visitor touch points, such as electronic devicesassociated with the online visitors, websites visited by the onlinevisitors, devices used by customer support representatives (for example,voice agents, chat agents, IVR systems, in-store agents, and the like)engaged by the online visitors, and the like. In an embodiment, thevisitor information extracted from various online visitor touch pointsincludes profile data and journey data corresponding to the respectiveonline visitor. The profile data may include profile information relatedto the online visitor, such as for example, an online visitor's name andcontact details, information related to products and services associatedwith the online visitor, social media account information, informationrelated to other messaging or sharing platforms used by the onlinevisitor, recent transactions, online visitor interests and preferences,online visitor's credit history, history of bill payments, credit score,memberships, history of travel, and the like. In some exemplaryembodiments, the visitor information may also include calendarinformation associated with the online visitor. For example, thecalendar information may include information related to an availabilityof the online visitor during the duration of the day/week/month.

In an embodiment, journey data received corresponding to the onlinevisitor may include information such as enterprise related web pagesvisited, queries entered, chat entries, purchases made, exit points fromwebsites visited, or decisions made, mobile screens touched, work flowsteps completed, sequence of steps taken, engagement time, IVR speechnodes touched, IVR prompts heard, widgets/screens/buttons selected orclicked, historical session experience and results, customerrelationship management (CRM) state and state changes, agent wrap-upnotes, speech recordings/transcripts, chat transcripts, survey feedback,channels touched/used, sequence of channels touched/used, instructions,information, answers, actions given/performed by either enterprisesystem or agents for the customer, and the like. In some examplescenarios, the journey data may include information related to pastinteractions of the online visitor with resources at a customer supportfacility, the types of channels used for interactions, customer channelpreferences, types of visitor issues involved, whether the issues wereresolved or not, the frequency of interactions and the like.

The channel interfaces of the communication module 308 may be configuredto receive such information related to the online visitors in real-timeor on a periodic basis. Moreover, the information may be received by thecommunication module 308 in an online mode or an offline mode. In anembodiment, the communication module 308 provides the receivedinformation to the database in the memory 304 for storage purposes. Inan embodiment, the information related to each customer is labeled withsome customer identification information (for example, a customer name,a unique ID and the like) prior to storing the information in thedatabase.

In an embodiment, various components of the apparatus 200, such as theprocessor 302, the memory 304, the I/O module 306 and the communicationmodule 308 are configured to communicate with each other via or througha centralized circuit system 310. The centralized circuit system 310 maybe various devices configured to, among other things, provide or enablecommunication between the components (302-308) of the apparatus 200. Incertain embodiments, the centralized circuit system 310 may be a centralprinted circuit board (PCB) such as a motherboard, a main board, asystem board, or a logic board. The centralized circuit system 310 mayalso, or alternatively, include other printed circuit assemblies (PCAs)or communication channel media.

It is noted that the apparatus 200 as illustrated and hereinafterdescribed is merely illustrative of an apparatus that could benefit fromembodiments of the invention and, therefore, should not be taken tolimit the scope of the invention. It is noted that the apparatus 200 mayinclude fewer or more components than those depicted in FIG. 3. In anembodiment, one or more components of the apparatus 200 may be deployedin a Web Server. In another embodiment, the apparatus 200 may be astandalone component in a remote machine connected to a communicationnetwork and capable of executing a set of instructions (sequentialand/or otherwise) to facilitate training of conversational agents of anenterprise. Moreover, the apparatus 200 may be implemented as acentralized system, or, alternatively, the various components of theapparatus 200 may be deployed in a distributed manner while beingoperatively coupled to each other. In an embodiment, one or morefunctionalities of the apparatus 200 may also be embodied as a clientwithin devices, such as online visitor's devices. In another embodiment,the apparatus 200 may be a central system that is shared by oraccessible to each of such devices.

As explained with reference to FIG. 1, online visitors to the enterpriseinteraction channels may seek interaction with agents for severalreasons. The term ‘interaction’, as explained with reference to FIG. 1,may correspond to a chat conversation or a voice conversation between aconversational agent and an online visitor visiting (i.e. accessing) anenterprise interaction channel. The conversational agent may be a liveagent (i.e. a human agent) or an automated agent (for example, achatbot).

The apparatus 200 is configured to facilitate interactions betweenagents and the online visitors by diverting the requests forinteractions received from the online visitors to appropriate agents. Aplurality of such interactions between the agents and the onlinevisitors may be conducted over a chat medium or a voice medium. Morespecifically, the plurality of interactions includes voice interactionsand textual chat interactions between the agents and the onlinevisitors.

In one embodiment, the content of the textual chat interactions may bereceived by the communication module 308 from Web/Data gathering serverslocated at the customer support center. The communication module 308 maybe configured to relay the information to the processor 302. Theprocessor 302 is configured to store the content of each textual chatinteraction as a textual transcript in the database associated with thememory 304 of the apparatus 200.

Some interactions between visitors and conversational agents may beconducted in a speech form and, in such scenarios, recorded voiceinteractions may be sent by the Web/Data gathering servers to thecommunication module 308. The communication module 308 may be configuredto relay the information to the processor 302. The recorded voiceinteractions may be converted into a textual form by the processor 302using Automatic Speech Recognition (ASR) and Natural Language Processing(NLP) techniques using special grammar stored in the memory 304. Theprocessor 302 may further be configured to store the textual content ofeach voice interaction as a textual transcript in the databaseassociated with the apparatus 200. To summarize, the content of eachinteraction between an agent and an online visitor is stored as atextual transcript in the database associated with the apparatus 200.

Accordingly, a plurality of textual transcripts may be generatedcorresponding to a plurality of interactions between agents of anenterprise and online visitors visiting the enterprise interactionchannels. For purposes of this description, the term ‘plurality ofinteractions’ as used herein implies any number of interactionsconducted within a predefined period (for example, a week, a month, ayear and so on and so forth). In an illustrative example, the user ofthe apparatus 200 may decide to choose interactions conducted within thelast three months as content material for training of agents tofacilitate persona-based agent interactions. Accordingly, the pluralityof interactions, in such a case, may imply all interactions conductedwithin the specified three-month period.

In at least one example embodiment, the processor 302 in conjunctionwith the instructions in the memory 304, is configured to cause theapparatus 200 to extract a plurality of persona related attributes froma textual transcript of each interaction. The plurality of personarelated attributes is extracted in relation to a persona of an onlinevisitor engaged in the respective interaction. The term ‘persona’ refersto characteristics reflecting behavioral patterns, goals, motives andpersonal values of the online visitor. It is noted that ‘personas’ asused herein is distinct from the concept of user profiles, that areclassically used in various kinds of analytics, where similar groups ofonline visitors are identified based on certain commonality in theirattributes, which may not necessarily reflect behavioral similarity, orsimilarity in goals and motives. An example of an online visitor personatype may be a ‘convenience customer’ that corresponds to a group ofonline visitors characterized by the behavioral trait that they arefocused and are looking for expeditious delivery of service. In anembodiment, a behavioral trait as referred to herein corresponds to abiological, sociological or a psychological characteristic. An exampleof a psychological characteristic may be a degree of decidednessassociated with an online visitor while making a purchase. For example,some online visitors dither for a long time and check out variousoptions multiple times before making a purchase, whereas some onlinevisitors are more decided in their purchasing options. An example of asociological characteristic may correspond to a likelihood measure of anonline visitor to socialize a negative sentiment or an experience. Forexample, an online visitor upon having a bad experience with a productpurchase may share his/her experience on social networks and/or complainbitterly on public forums, whereas another online visitor may choose toreturn the product and opt for another product, while precludingsocializing his/her experience. An example of a biologicalcharacteristic may correspond to gender or even age-based inclinationtowards consumption of products/services or information. For example, amiddle-aged female may be more likely to purchase a facial productassociated with aging, whereas a middle-aged man may be more likely topurchase a hair care related product. It is understood that examples ofonline visitors' biological, sociological and psychologicalcharacteristics are provided herein for illustrative purposes and maynot be considered limiting the scope of set of behavioral traitsassociated with a persona type and that each persona type may includeone or more such behavioral traits. The extraction of the plurality ofpersona related attributes related to an online visitor from a textualtranscript of each interaction is explained hereinafter.

Each textual transcript includes a plurality of utterances exchangedbetween an agent and an online visitor engaged in the interaction. Theterm ‘utterance’ as used throughout the description refers to a contentassociated with a single ‘turn’ in a turn-based interaction between anagent and the online visitor. It is understood that an interactionbetween an agent and an online visitor may include several turns, i.e.the online visitor and the agent may take turns in conversing with eachother. The content of interaction (whether in typed or spoken format)associated with a single turn (either from an agent or a visitor) isreferred to herein as an utterance. Accordingly, an interaction betweenthe agent and the online visitor may include a plurality of utterances.

In an embodiment, the processor 302 is configured to extract a pluralityof utterances of the online visitor from the textual transcript of therespective interaction. The plurality of utterances corresponding to theonline visitor may then be used to extract persona attributescorresponding to the respective online visitor. In one embodiment, theprocessor 302 is configured to perform a predefined personality traitevaluation for each extracted utterance to facilitate extraction of theplurality of persona related attributes from each interaction. Forexample, the predefined personality trait evaluation may include atleast one of big-five personality factors based evaluation and thirtypersonality related facets based evaluation. The personality traitevaluation based on big-five personality factors is explainedhereinafter. For purposes of description, the term ‘online visitor’ isinterchangeably referred to as ‘visitor’ hereinafter.

In one example embodiment, each utterance of the visitor is subjected tobig-five personality factors based evaluation. It is noted that the bigfive personality factors are (1) Openness, (2) Conscientiousness, (3)Extroversion, (4) Agreeableness and (5) Neuroticism. Evaluatingpersonality factors or traits of an online visitor based on the big-fivemodel enables a measurement of how individuals interact with theirsurroundings. An attribute may be extracted corresponding to ameasurement of an online visitor's personality for each personalityfactor from among the big-five personality factors.

In an illustrative example, the processor 302 may be configured toclassify each visitor utterance in an interaction as indicative of oneof the big-five personality factors and rate them on a scale, forexample on a linear scale of ‘1’ to ‘5’, with ‘1’ being least relevantand ‘5’ being most relevant. For example, an utterance ‘Yes, I wouldlike to try out the new offer on wireless headphones’ may be classifiedas being related to personality factor ‘Openness’ (i.e. the visitor isopen to new experiences) and the utterance may be rated as ‘5’ on thescale of 1-5 for the personality factor: ‘Openness’. Similarly, anotherutterance ‘I have called several times with no assistance from yourside. Are you guys serious in assisting people who have bought yourproduct?’ may be classified as being related to personality factor‘Neuroticism’ (as the visitor seems to be in an angry or frustratedmood, i.e. not emotionally stable) and the utterance may be rated as ‘3’on the scale of 1-5 for the personality factor: ‘Neuroticism’.Accordingly, each visitor utterance may be classified and rated as per abig-five personality factor. For example, visitor utterance numbers 1and 5 (i.e. the first and the fifth utterance) in an interaction may berated for personality trait ‘Extroversion’, whereas, utterance numbers 2and 9 (i.e. the second and ninth utterances) in a conversation may berated for personality factor ‘Agreeableness’ and so on and so forth.

The ratings for individual utterances corresponding to each personalityfactor may then be combined to arrive at a score for each personalitytrait. For example, if visitor utterances 3, 7 and 8 in a conversationare rated for big-five personality factor ‘Conscientiousness’, and, theindividual utterances are associated with rating 3, 4 and 5,respectively, then the score for the personality trait‘Conscientiousness’ may be computed as a sum of the ratings for thethree utterances, i.e. the score will be 3+4+5=12. It is noted that someutterances may have a negative connotation of a particular personalitytrait, then such an utterance may be rated on a reversed rating scale(with 5 being least relevant and 1 being most relevant) and the ratingmay be associated with a negative sign (i.e. the rating will besubtracted while computing the overall score). More specifically, if anutterance has a rating of ‘4’ on the reversed rating scale on account ofhaving an opposite connotation to a personality factor, then duringcomputation of the score for the personality factor, this rating may besubtracted during combination of the ratings for computing the scorecorresponding to the personality factor. In an embodiment, the scoresfor each of the big-five personality factors may be utilized asattributes for use in generation of a feature vector datarepresentation. In the simplest form, if a big-five personality factorscore is above a predefined threshold say ‘50’, then the attribute maybe represented by a binary ‘1’, else, it may be represented by ‘0’. Itis noted that the binary representation is mentioned herein forillustration purposes and that the big-five personality factor scoresmay be represented in any form (for example, a vector representation ofpredefined length) to configure a persona related attribute for arespective online visitor.

Additionally, there are 30 personality related facets for which anevaluation of each visitor may be performed. These thirty facets relateto Imagination, Artistic interests, Depth of emotions, Willingness toexperiment, Intellectual curiosity, Tolerance for diversity, Sense ofcompetence, Orderliness, Sense of responsibility, Achievement striving,Self-discipline, Deliberateness, Warmth, Gregariousness, Assertiveness,Activity, Level of excitement-seeking, Positive emotions, Trust inothers, Sincerity, Altruism, Compliance, Modesty, Sympathy, Anxiety,Angry Hostility, Moodiness/Contentment, Self-consciousness,Self-indulgence and Sensitivity to stress. Optionally, there may beadditional attributes defined for ‘Needs and Values’.

In one embodiment, an attribute may be extracted corresponding to ameasurement of a visitor's personality for each facet from among the 30personality related facets listed above. Accordingly, in addition tofive attributes configured based on the visitor's big-five personalityfactors, thirty attributes may be configured corresponding to themeasurement of a visitor's personality for each facet from among thethirty personality related facets.

In at least one example embodiment, the processor 302 in conjunctionwith the instructions in the memory 304, is configured to generate afeature vector data representation based, at least in part, on theplurality of persona related attributes extracted from each interaction.A feature vector data representation may correspond to a vectorrepresentation of predefined length (for example, 200 or 300 length).Since a feature vector data representation is generated based on theplurality of persona related attributes extracted from each interaction,each feature vector data representation may be representative of thepersona of an online visitor. In other words, each feature vector datarepresentation may correspond to an online visitor, who has engaged inan interaction with agent.

As explained above, the feature vector data representation is generated,based, at least in part, on the plurality of persona related attributesextracted from each interaction. More specifically, in addition to thepersona related attributes, some other attributes of the online visitormay also be considered while generating the feature vector datarepresentation. For example, in addition to the thirty-five personarelated attributes now configured, other attributes related to visitor'sbehavior, such as for example, how often the visitor purchases aproduct, whether the visitor has an inclination for buying new product,whether the visitor has an inclination to chat, what NPS/CSAT scores thevisitor generally provides, and the like may be predicted/identifiedusing text-mining algorithms and intent prediction algorithms. Morespecifically, Machine Learning (ML) models stored in the memory 304 maybe used by the processor 302 to predict (1) Net Promoter Score(NPS)/Customer Satisfaction (CSAT) score, (2) overall satisfaction asmeasured by last few utterances of the visitor and (3) possibility ofup-sale/cross-sale as obtained from Sales-order report and (4)probability of clicking on on-domain personalized banner for newproducts and (5) probability of clicking off-domain banners meant forretargeting a visitor, and the like. The score from these five ML modelsmay be used to configure a corresponding attribute. Accordingly, aplurality of attributes corresponding to the visitor may be extracted inaddition to the persona related attributes.

In one embodiment, the attributes represented as ‘1’s or ‘0’s may bearranged in a string form to configure a contiguous sequence of ‘1’s and‘0’s totaling a predefined number (100 or 200 binary digits forexample). The predefined number may be arrived at, based on the numberof personality-based attributes. For example, if 100 attributes areextracted, then a feature vector data representation may be configuredof ‘100’ vector length. The processor 302 is configured to generate afeature vector data representation for each visitor, for whom personarelated attributes are extracted based on the visitor's respectiveinteraction with the agent. In an example scenario, a plurality offeature vector data representations of predefined length may begenerated corresponding to the plurality of visitors.

Referring now to FIG. 4, a simplified representation of a table 400 isshown for illustrating feature vector data representations generatedcorresponding to the plurality of visitors, in accordance with anembodiment of the invention. The table 400 includes a plurality ofcolumns, such as column 402, 404, 406 and 408. The column 402 includes alist of all visitors, such as visitor 1, visitor 2 and so on and soforth till visitor N, who have engaged in interactions with agents ofthe enterprise on online enterprise interaction channels. The columns404, 406 to 408 represent persona related attributes (shown as ATTRIBUTE1, ATTRIBUTE 2 to ATTRIBUTE N, respectively) extracted from theinteractions in relation to the online visitors and their respectiveactivities on the online enterprise channels. As explained withreference to FIG. 3, several persona related attributes may be extractedby performing predefined personality trait evaluation of visitorutterances. Additionally, other attributes related to visitor'sbehavior, such as for example, how often the visitor purchases aproduct, whether the visitor has an inclination for buying new product,whether the visitor has an inclination to chat, what NPS/CSAT scores thevisitor generally provides, and the like may be extracted. The entriesin the columns record the attribute values for the correspondingattribute for each visitor. It is noted that though binary values areshown as entries in the columns 404, 406 to 408, in at least someembodiments, each entry may correspond to a vector (or a numericalvalue) of fixed length. The entries in each column from 404 to 408 foreach visitor configure a feature vector data representation 410 for therespective visitor. As can be seen, a plurality of feature vector datarepresentation may be generated corresponding to a plurality ofvisitors.

Referring back to FIG. 3, in one embodiment, the processor 302 may beconfigured to facilitate defining of a plurality of persona-basedclusters. In one embodiment, the optimal number of predefinedpersona-based clusters may be determined by maximizing a Silhouettescore or observing the variation of intra-cluster distance as a functionof number of clusters. Alternatively, the user of the apparatus 200 maybe configured to define a plurality of persona-based clusters (i.e. acluster for each type of persona) and provide attributes related to thecluster. The processor 302 may be configured to generate at least onecluster feature vector corresponding to each persona-based cluster basedon the attributes related to the person-based clusters defined by theuser. The cluster feature vector is indicative of the visitor personarepresentative of the respective persona-based cluster.

In at least one example embodiment, the processor 302 in conjunctionwith the instructions in the memory 304, is configured to cause theapparatus 200 to classify the plurality of feature vector datarepresentations based on a plurality of persona-based clusters. It isnoted that classifying the plurality of feature vector datarepresentations based on the plurality of persona-based clusters enablesclassification of the plurality of online visitors into the plurality ofpersona-based clusters. The classification of the plurality of featurevector data representations may be performed using a clusteringalgorithm capable of computing a similarity or a dissimilarity measure(such as a distance metric for instance) between the cluster featurevector of each persona-based cluster and each feature vector datarepresentation from among the plurality of feature vector datarepresentations to classify the plurality of feature vector datarepresentation into the plurality of persona-based clusters. Somenon-limiting examples of metrics used to compare the feature vector datarepresentation and the cluster feature vector may include distancemeasuring metrics like cosine similarity, Manhattan distance, Euclideandistance, optimal matching, longest common subsequence, longest commonprefix, hamming distance etc. and the like. More specifically, visitorswhose feature vectors are substantially close to a cluster center of apersona-based cluster may be classified (i.e. grouped) in thecorresponding persona-based cluster. Accordingly, the plurality ofvisitors may be grouped into persona-based clusters based on thecorresponding feature vector data representations capturing theirrespective persona. More specifically, all visitors who have similarpersona are grouped into a persona-based cluster.

In at least one example embodiment, the processor 302 in conjunctionwith the instructions stored in the memory 304, is configured to causethe apparatus to train a learning model for each persona-based clusterby using utterances of online visitors classified into a respectivepersona-based cluster. More specifically, conversations related to eachvisitor classified in a persona-based cluster may be fetched and used totrain a deep learning neural network model, such as a Recurrent NeuralNetwork (RNN) model. In one embodiment, for each persona-based cluster,a set of textual transcripts are chosen based on (1) intent of thevisitor and (2) proximity to the cluster center. Thus, interactions thatare not sufficiently close to the cluster center are discarded tocontrol quality of content used for subsequent model training. Inaddition, only those interactions are taken where the disposition ispositive, i.e. the visitors are satisfied with the outcome and the wayagents handled the conversation. Training an RNN model usinginteractions related to several visitors having similar persona mayenable the RNN model to mimic visitor's persona, which may then be usedto train conversational agents. In some embodiments, a conversationalagent may be trained to interact with several RNN models to imbibeseveral visitor personas. Such training of conversational agents enablesthe conversational agents to handle a variety of requests from aplurality of visitors associated with different personas. For example,conversations related to visitors of a particular type of persona may beused to train an RNN model to predict a previous utterance or asubsequent utterance. For example, using the utterances in theinteractions of the visitors classified in one type of persona-basedcluster, the RNN model may be trained to predict a previous agentutterance or a subsequent agent utterance for a given visitor utteranceinput to the RNN model. In another illustrative example, using theutterances in the interactions of the visitors classified in one type ofpersona-based cluster, the RNN model may be trained to predict aprevious visitor utterance or a subsequent visitor utterance for a givenagent utterance input to the RNN model. Such training of the RNN modelsenables effective training of the conversational agents.

In one embodiment, the processor 302, subsequent to receiving a requestfor an agent interaction, i.e. a request for conversation with an agentof an enterprise, may use text-mining or intent prediction algorithmsstored in the memory 304 to predict a persona of the visitor seekingagent interaction. As explained with reference to FIG. 3, visitor's pastinteractions with the enterprise and the current journey on theenterprise interaction channel may be used to predict a persona of thevisitor. Subsequent to the prediction of the persona of the visitorassociated with the requested agent interaction, the processor 302 mayassign an automated conversational agent trained on handlinginteractions for that particular-persona type to engage with thevisitor. As the automated conversational agent is trained for handlingsuch persona-based interactions, the responses provided by the automatedconversational agent may be more streamlined and accurate andinvolvement of live agents in overriding the automated conversationalagent responses may be drastically reduced. In some embodiments, the RNNmodel is used for facilitating training of live agents in interactingwith future online visitors predicted to be associated with visitorpersona substantially matching the visitor persona mimicked by thelearning model.

FIG. 5 shows a representation of an example process flow 500 forfacilitating persona-based training of learning models, in accordancewith an embodiment of the invention. In one embodiment, the varioussteps of the process flow 500 may be performed by the apparatus 200explained with reference to FIGS. 2 and 3. Alternatively, the varioussteps of the process flow 500 may be performed by a system capable ofexecuting the instructions executed by a processor, such as theprocessor 302, for facilitating persona-based agent interactions withonline visitors.

The process flow 500 is depicted to start at 502. At 502, utterancesrelated to a plurality of visitors are extracted from respectiveinteractions of the visitors with the conversational agents of theenterprise. For example, all visitor lines in a chat between a visitorand a human chat agent may be extracted. At 504 of the process flow 500,attributes are extracted for each visitor from the respective visitorlines and a feature vector data representation is generatedcorresponding to each visitor. The extraction of the attributes and thesubsequent generation of the feature vector data representation may beperformed as explained with reference to FIGS. 3 and 4 and is notexplained again herein.

At 506 of the process flow 500, a clustering of the feature vector datarepresentations is performed to cluster (or segregate) the plurality ofvisitors into a plurality of persona-based clusters. The clustering ofthe feature vector data representations may result in categorizingvisitors into different clusters. In the FIG. 5, the clustering of thefeature vector data representations is depicted to have generatedcluster 508 (shown as ‘Cluster 1’ including ‘X’ visitors), cluster 510(shown as ‘Cluster 2’ including ‘Y’ visitors) to cluster 512 (shown as‘Cluster ‘N’ including ‘Z’ visitors), where X, Y and Z are positiveintegers.

Further, the visitor utterances in the interactions (for example, chatconversations or voice conversations) of the visitors categorized ineach persona-based cluster may be used to train a deep learning neuralnetwork (such as an RNN) to mimic a visitor persona associated with thecorresponding cluster. Accordingly, as shown in FIG. 5, eachpersona-based cluster is associated with an RNN model implying thatvisitor utterances from the interactions of the visitors classified inthat persona-based cluster are used to train the RNN models for acorresponding persona to facilitate mimicking behavior of the visitorassociated with the same persona. Accordingly, the cluster 508 isdepicted to be associated with an RNN model 514 for training of alearning model to mimic ‘persona 1’, the cluster 510 is depicted to beassociated with an RNN model 516 for training of a learning model tomimic ‘persona 2’, and the cluster 512 is depicted to be associated withan RNN model 518 for training of a learning model to mimic ‘persona N’.

The training of learning models using utterances of visitors extractedfrom interactions is explained hereinafter.

Referring now to FIGS. 6, 7 and 8, block diagrams are depicted toillustrate an example training of the learning models, in accordancewith an embodiment of the invention. More specifically, FIG. 6 depicts aproviding of an agent utterance 602 to an RNN encoder 604 resulting inan output corresponding to the hidden state representation of theencoder, depicted as a hidden state 606. It is noted that the hiddenstate 606 captures the context of the agent utterance in theinteraction. The hidden state 606 may be provided as an input to an RNNdecoder 608. The RNN decoder 608 may be trained using machine learningalgorithms and datasets corresponding to set of interactions withsimilar personas to predict a previous visitor utterance 610. As thehidden state 606 captures the context of the utterance in theinteraction, the RNN decoder 608 may be trained to decode the contextand predict the previous visitor utterance 610, which resulted in theagent utterance 602. Similarly, the RNN decoder 608 may be trained topredict a next visitor utterance as exemplarily depicted in FIG. 7. Morespecifically, upon receiving the hidden state 606 capturing the contextof the agent utterance 602, the RNN decoder 608 may be trained to decodethe context and predict a next visitor utterance 612. In someembodiments, the RNN decoder 608 may be trained to predict both theprevious visitor utterance 610 and the next visitor utterance 612 asexemplarily depicted in FIG. 8.

It is noted that training of the RNN model may not be limited todecoding context in agent utterances. In at least some exampleembodiments, the RNN decoder 608 may be trained to decode the context ina hidden state representing a visitor utterance and predict the previousagent utterance that resulted in such a visitor utterance as well as thenext agent utterance that may result from the providing of such avisitor utterance.

The trained model embodied as the RNN encoder 604 and the RNN decoder608 may, in effect, configure an automated conversational agent (such asa chatbot) which can mimic a visitor associated with a known persona.The chatbots may thereafter be used to engage in conversations with liveagents to train the live agents to engage with future online visitorswith the known persona. It is noted that the datasets created bycategorizing conversations with similar personas may also enabletraining of automated conversational agents in engaging with visitors.For example, a plurality of agent persona related attributes may beextracted from utterances of the agents engaged in the plurality ofinteractions and may be processed in a similar manner as explained withreference to persona related attributes of the visitor to train learningmodels, like the RNN models, to mimic the agent's persona. The trainedRNN model may configure an automated conversational agent capable ofhandling visitor queries and providing desired assistance to thevisitors. An example response to a visitor query generated by a trainedchatbot, is shown in FIG. 9.

FIG. 9 shows a block diagram representation 900 for illustratinggeneration of a sequential output of words configuring theconversational agent response in response to a visitor query, inaccordance with an embodiment of the invention.

As explained with reference to FIGS. 6 to 8, the processor 302 of theapparatus 200 (shown in FIG. 3) may use the set of textual transcriptsof interactions associated with visitors categorized in a particularpersona-based cluster to train learning model, which in turn may be usedto configure automated conversational agents. More specifically, an RNNmodel including an encoding logic and a decoding logic may be trainedusing the interactions to retain context and predict utterances andthereby respond appropriately to visitor/agent utterances.

The encoding logic of the RNN model is used to encode, or in otherwords, generate a vector (for example, a numerical value of fixedlength) for each word sequentially fed to the encoding logic, whereasthe decoding logic is used to decode, or in other words, generate a wordresponse (more specifically, a numerical vector representing aprobability distribution over the vocabulary) for each word sequentiallyfed to the decoding logic.

The encoding logic of the RNN model is exemplarily represented usingblock 902, referred to hereinafter as an ‘RNN Encoder 902’, whereas thedecoding logic is exemplarily represented using block 904, referred tohereinafter as an ‘RNN Decoder 904’. As can be seen the words of avisitor query 910, i.e. words ‘WREN’, ‘IS’, ‘MY’, ‘CONTRACT’ AND‘EXPIRING’ are sequentially provided to the RNN encoder 902.

It is noted that the multiple RNN encoders are shown to be arranged in apipeline manner for illustration purposes. Only one RNN encoder 902typically receives the words one after another. After each word passesthrough the RNN encoder 902, a vector is generated. The vector or thenumerical value is indicative of the state of the RNN representing allwords that have been provided to the RNN encoder 902 so far. The nextword changes the state of the RNN, which corresponds to another vector.When all the words in the visitor query 910 are sequentially provided tothe RNN encoder 902, the final output which is shown as a ‘contextvector 906’ represents the state of the RNN encoder 902 upon beingsequentially provided all the words in the visitor query 910.

As shown, the context vector 906 is then provided the RNN decoder 904,which provides a vector representation configuring the first word of theconversational agent response, shown as ‘CAN’. The word is provided tothe RNN decoder 904 to generate the second word ‘I’ and so on and soforth to generate the sequential output of words configuring aconversational agent response 920: ‘CAN I HAVE YOUR PHONE NUMBER?’ Theresponse is then provided as reply to the visitor.

It is noted that the RNN model as described with reference to FIGS. 6 to8 only predicts an agent utterance (or a visitor utterance) for a givenvisitor utterance (or an agent utterance) and as such, the RNN modeldoes not take the context of the previous utterances into account forprediction of agent/visitor utterances. An example RNN modelarchitecture capable of taking into account the context of theconversation into account is explained with reference to FIG. 10.

FIG. 10 shows a simplified block-diagram representation 1000 of anarchitecture of an RNN model for facilitating persona-based training oflearning models, in accordance with an embodiment of the invention.

In one embodiment, the processor 302 of the apparatus 200 (shown in FIG.3) may be configured to select two or more utterances from aconversation of a visitor classified in a particular persona-basedcluster based on a width value of a moving window. The term ‘movingwindow’ as used herein implies an imaginary bounding box of fixed widthcapable of being slid over textual representation of a turn-basedinteraction to capture a fixed number of conversational lines. Forexample, for the width value of the moving window selected as three,three utterances in the turn-based interaction may be selected forpredicting each agent/visitor utterance.

The representation 1000 depicts three utterances provided as inputs tothree RNN encoders. It is noted that three RNN encoders, implying amoving window width value of three for encoding three utterances isshown herein for illustration purposes and that the number of RNNencoders may vary as per the selection of moving window width value. Forexample, the width value of moving window may be selected to be anynumber greater than 1.

Each RNN encoder (i.e. RNN encoding logic) is configured to receive oneutterance as an input and generate a vector representation by encodingthe utterance. As explained with reference to FIG. 9, each word in theutterance may be sequentially fed to the RNN encoder to generate anumerical value (which serves as a vector representation of theutterance). In an example embodiment, the current visitor utterance maybe provided to one RNN encoder, the latest agent utterance may beprovided to the second RNN encoder and the previous visitor utterancemay be provided to the third RNN encoder. Accordingly, an RNN encoder1002 is depicted to receive the current visitor utterance U_(N), an RNNencoder 1004 is depicted to receive the latest agent utterance U_(N-1)and an RNN encoder 1006 is depicted to receive the previous visitorutterance U_(N-2). The vector outputs of the RNN encoders 1002, 1004 and1006 are depicted to be V_(N), V_(N-1), and V_(N-2), respectively. Thevector outputs of the RNN encoders 1002-1006 are depicted to be providedto a first Artificial Neural Network (ANN) 1010 (i.e. multi-layerperceptron logic retrieved by the processor 302). The first ANN 1010 isconfigured to receive the outputs of the RNN encoders 1002-1006 andgenerate a final encoded output, depicted as O_(E). It is noted a dottedblock 1020 is shown in FIG. 10 to illustrate the encode-relatedprocessing performed by the processor 302 of the apparatus 200.

The final encoded output O_(E) is provided to the decoding module. Morespecifically, the final encoded output O_(E) is provided to an RNNdecoder 1050, which is configured to generate a decoded output O_(D).The decoded output O_(D) is provided to a second Artificial NeuralNetwork (ANN) 1070 configured to generate a word for each decoded outputreceived from the RNN decoder 1050, thereby generating the wordsconfiguring a conversational agent response 1090 (such as the agentresponse 920 shown in FIG. 9). It is noted a dotted block 1060 is shownin FIG. 10 to illustrate the decode-related processing performed by theprocessor 302 of the apparatus 200.

The conversational agent response 1090 is then provided by the processor302 to the communication module 308 (shown in FIG. 3), which isconfigured to forward the response to the conversational agent. Theconversational agent may then provide the conversational agent response1090 to the visitor as a reply to the visitor's query. The prediction ofeach word in the conversational agent response by encoding and decodingseveral utterances in the turn-based interaction improves a quality ofresponses provided to the visitor.

A method for facilitating persona-based agent interactions with onlinevisitors is explained next with reference to FIG. 11.

FIG. 11 shows a flow diagram of a method 1100 for facilitatingpersona-based agent interactions with online visitors, in accordancewith an embodiment of the invention. The method 1100 depicted in theflow diagram may be executed by, for example, the apparatus 200explained with reference to FIGS. 2 to 10. Operations of the flowchart,and combinations of operation in the flowchart, may be implemented by,for example, hardware, firmware, a processor, circuitry and/or adifferent device associated with the execution of software that includesone or more computer program instructions. The operations of the method1100 are described herein with help of the apparatus 200. It is notedthat, the operations of the method 1100 can be described and/orpracticed by using any system other than the apparatus 200. The method1100 starts at operation 1102.

At operation 1102 of the method 1100, a plurality of persona relatedattributes are extracted from a textual transcript of each interactionfrom among a plurality of interactions between agents of an enterpriseand a plurality of online visitors visiting enterprise interactionchannels. The plurality of persona related attributes is extracted fromeach interaction, by a processor such as the processor 302 explainedwith reference to FIGS. 3 to 10, in relation to a persona of an onlinevisitor engaged in the respective interaction.

In one embodiment, a plurality of textual transcripts are generatedcorresponding to a plurality of interactions between agents and onlinevisitors of an enterprise. The content of textual chat interactionconfigures a textual transcript corresponding to the respectiveinteraction, whereas for voice interactions, the recorded content isconverted into a textual form using Automatic Speech Recognition (ASR)and Natural Language Processing (NLP) techniques using special grammarto configure a textual transcript corresponding to the respectiveinteraction. A plurality of persona related attributes is extracted froma textual transcript of each interaction. The term ‘persona’ refers tocharacteristics reflecting behavioral patterns, goals, motives andpersonal values of the online visitor and is explained with reference toFIG. 3.

Each textual transcript includes a plurality of utterances exchangedbetween an agent and an online visitor engaged in the interaction. In anembodiment, a plurality of utterances of the online visitor areextracted from the textual transcript of the respective interaction. Theplurality of utterances corresponding to the online visitor are thenused to extract persona attributes corresponding to the respectiveonline visitor. In one embodiment, a predefined personality traitevaluation is performed for each extracted utterance to facilitateextraction of the plurality of persona related attributes from eachinteraction. For example, the predefined personality trait evaluationmay include at least one of big-five personality factors basedevaluation and thirty personality related facets based evaluation. Thepersonality trait evaluation based on big-five personality factors basedevaluation and thirty personality related facets is explained withreference to FIG. 3 and is not explained again herein. In oneembodiment, an attribute is extracted corresponding to a measurement ofa visitor's personality for each trait to configure a plurality ofpersona related attributes.

At operation 1104 of the method 1100, a feature vector datarepresentation is generated by the processor based, at least in part, onthe plurality of persona related attributes extracted from eachinteraction. More specifically, in addition to the persona relatedattributes, some other attributes of the online visitor may also beconsidered while generating the feature vector data representation. Forexample, in addition to the thirty-five persona related attributes nowconfigured, other attributes related to visitor's behavior, such as forexample, how often the visitor purchases a product, whether the visitorhas an inclination for buying new product, whether the visitor has aninclination to chat, what NPS/CSAT scores the visitor generallyprovides, and the like may be predicted/identified using text-miningalgorithms and intent prediction algorithms. More specifically, MachineLearning (ML) models stored in the memory 304 may be used by theprocessor 302 to predict (1) Net Promoter Score (NPS)/CustomerSatisfaction (CSAT) score, (2) overall satisfaction as measured by lastfew lines of visitor and (3) possibility of up-sale/cross-sale asobtained from Sales-order report and (4) probability of clicking onon-domain personalized banner for new products and (5) probability ofclicking off-domain banners meant for retargeting a visitor, and thelike. The score from these five ML models may be used to configure acorresponding attribute. Accordingly, a plurality of attributescorresponding to the visitor may be extracted in addition to the personarelated attributes.

In one embodiment, the attributes represented as ‘1’s or ‘0’s may bearranged in a string form to configure a contiguous sequence of ‘1’s and‘0’s totaling a predefined number (100 or 200 binary digits forexample). The predefined number may be arrived at, based on the numberof personality-based attributes. For example, if 100 attributes areextracted, then a feature vector data representation may be configuredof ‘100’ vector length. The processor is configured to generate afeature vector data representation for each visitor, for whom personarelated attributes are extracted based on their respective interactionswith the agents. In an example scenario, a plurality of feature vectordata representations of predefined length may be generated correspondingto the plurality of visitors. The generation of the feature vector datarepresentation may be performed as explained with reference to FIG. 4and is not explained again herein.

At operation 1106 of the method 1100, the plurality of feature vectordata representations are classified based on a plurality ofpersona-based clusters. The classification of the plurality of featurevector data representations based on the plurality of persona-basedclusters enables classification of the plurality of online visitors intothe plurality of persona-based clusters. The classification of theplurality of feature vector data representations may be performed usinga clustering algorithm capable of computing a similarity or adissimilarity measure (such as a distance metric for instance) betweenthe cluster feature vector of each persona-based cluster and individualfeature vector data representation from among the plurality of featurevector data representations to classify the plurality of feature vectorsinto the plurality of persona-based clusters. Some non-limiting examplesof metrics used to compare the feature vectors may include distancemeasuring metrics like cosine similarity, Manhattan distance, Euclideandistance, optimal matching, longest common subsequence, longest commonprefix, hamming distance etc. and the like. More specifically, visitorswhose feature vectors are substantially close to a cluster center of apersona-based cluster may be classified (i.e. grouped) in thecorresponding persona-based cluster. Accordingly, the plurality ofvisitors may be grouped into persona-based clusters based on thecorresponding feature vector data representations capturing theirrespective persona. More specifically, all visitors who have similarpersona are grouped into a persona-based cluster.

At operation 1108 of the method 1100, a learning model is trained foreach persona-based cluster by the processor by using utterances ofonline visitors classified into a respective persona-based cluster. Thelearning model is trained to mimic a visitor persona representative ofthe respective persona-based cluster. More specifically, interactionsrelated to each visitor classified in a persona-based cluster may befetched and used to train a deep learning neural network model, such asa Recurrent Neural Network (RNN) model. In one embodiment, for eachpersona-based cluster, a set of textual transcripts are chosen based on(1) intent of the visitor and (2) proximity to the cluster center. Thus,interactions that are not sufficiently close to the cluster center arediscarded to control quality of content used for subsequent modeltraining. In addition, only those interactions are taken where thedisposition is positive, i.e., the visitors are satisfied with theoutcome and the way agents handled the conversation. Training an RNNmodel using interactions related to several visitors having similarpersona may enable the RNN model to mimic visitor's persona, which maythen be used to train conversational agents.

In one embodiment, subsequent to receiving a request for an agentinteraction, i.e. a request for conversation with an agent of anenterprise, the processor may use text-mining or intent predictionalgorithms to predict a persona of the visitor seeking agentinteraction. As explained with reference to FIG. 3, visitor's pastinteractions with the enterprise and the current journey on theenterprise interaction channel may be used to predict a persona of thevisitor. Subsequent to the prediction of the persona of the visitorassociated with the requested agent interaction, the processor mayassign an automated conversational agent trained on handlinginteractions for that particular-persona type to engage with thevisitor. As the automated conversational agent is trained for handlingsuch persona-based interactions, the responses provided by the automatedconversational agent may be more streamlined and accurate andinvolvement of live agents in overriding the automated conversationalagent responses may be drastically reduced. In some embodiments, the RNNmodel is used for facilitating training of live agents in interactingwith future online visitors predicted to be associated with visitorpersona substantially matching the visitor persona mimicked by thelearning model.

Various embodiments disclosed herein provide numerous advantages. Thetechniques disclosed herein suggest techniques for training ofconversational agents to enable the conversational agents to handlevisitor interactions involving various visitor personas. The models aretrained to incorporate persona in automated conversational agents.Incorporating persona in automated conversational agents may bebeneficial in two different ways. An automated conversational agentmimicking visitors' persona may be used for live agent training sincedifferent visitors have different personalities and even for similarissues can react differently, while chatting with live agents. Forexample, some visitors may be lenient and patient while some visitorscan be highly demanding. Persona-based agent training will equip liveagents to handle online visitors in an efficient manner. Similarly, alive agent's persona may be incorporated in an automated conversationalagent and such a trained automated conversational agent may be used forgreater compatibility while conversing with a visitor with knownpersonality traits.

Although the present invention has been described with reference tospecific exemplary embodiments, it is noted that various modificationsand changes may be made to these embodiments without departing from thebroad spirit and scope of the present invention. For example, thevarious operations, blocks, etc., described herein may be enabled andoperated using hardware circuitry (for example, complementary metaloxide semiconductor (CMOS) based logic circuitry), firmware, softwareand/or any combination of hardware, firmware, and/or software (forexample, embodied in a machine-readable medium). For example, theapparatuses and methods may be embodied using transistors, logic gates,and electrical circuits (for example, application specific integratedcircuit (ASIC) circuitry and/or in Digital Signal Processor (DSP)circuitry).

Particularly, the apparatus 200 and its various components such as theprocessor 302, the memory 304, the I/O module 306, the communicationmodule 308, the centralized circuit system 310 may be enabled usingsoftware and/or using transistors, logic gates, and electrical circuits(for example, integrated circuit circuitry such as ASIC circuitry).Various embodiments of the present invention may include one or morecomputer programs stored or otherwise embodied on a computer-readablemedium, wherein the computer programs are configured to cause aprocessor or computer to perform one or more operations (for example,operations explained herein with reference to FIGS. 8 and 9). Acomputer-readable medium storing, embodying, or encoded with a computerprogram, or similar language, may be embodied as a tangible data storagedevice storing one or more software programs that are configured tocause a processor or computer to perform one or more operations. Suchoperations may be, for example, any of the steps or operations describedherein. In some embodiments, the computer programs may be stored andprovided to a computer using any type of non-transitory computerreadable media. Non-transitory computer readable media include any typeof tangible storage media. Examples of non-transitory computer readablemedia include magnetic storage media (such as floppy disks, magnetictapes, hard disk drives, etc.), optical magnetic storage media (e.g.,magneto-optical disks), CD-ROM (compact disc read only memory), CD-R(compact disc recordable), CD-R/W (compact disc rewritable), DVD(Digital Versatile Disc), BD (Blu-ray (registered trademark) Disc), andsemiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM(erasable PROM), flash ROM, RAM (random access memory), etc.).Additionally, a tangible data storage device may be embodied as one ormore volatile memory devices, one or more non-volatile memory devices,and/or a combination of one or more volatile memory devices andnon-volatile memory devices. In some embodiments, the computer programsmay be provided to a computer using any type of transitory computerreadable media. Examples of transitory computer readable media includeelectric signals, optical signals, and electromagnetic waves. Transitorycomputer readable media can provide the program to a computer via awired communication line (e.g., electric wires, and optical fibers) or awireless communication line.

Various embodiments of the present invention, as discussed above, may bepracticed with steps and/or operations in a different order, and/or withhardware elements in configurations, which are different than thosewhich, are disclosed. Therefore, although the invention has beendescribed based upon these exemplary embodiments, it is noted thatcertain modifications, variations, and alternative constructions may beapparent and well within the spirit and scope of the invention.

Although various exemplary embodiments of the present invention aredescribed herein in a language specific to structural features and/ormethodological acts, the subject matter defined in the appended claimsis not necessarily limited to the specific features or acts describedabove. Rather, the specific features and acts described above aredisclosed as exemplary forms of implementing the claims.

The invention claimed is:
 1. A computer-implemented method forfacilitating persona-based agent interactions, the method comprising:extracting, by a processor, a plurality of persona related attributescomprising characteristics reflecting behavioral patterns, goals,motives, and personal values of an online visitor from a textualtranscript of each interaction from among a plurality of interactionsbetween agents of an enterprise and a plurality of online visitorsvisiting enterprise interaction channels, the plurality of personarelated attributes extracted from each interaction in relation to apersona of an online visitor engaged in the respective interaction;generating, by the processor, a feature vector data representationbased, at least in part, on the plurality of persona related attributesextracted from each interaction, wherein generating the feature vectordata representation in relation to each interaction from among theplurality of interactions configures a plurality of feature vector datarepresentations; classifying, by the processor, the plurality of featurevector data representations based on a plurality of persona-basedclusters, wherein classifying the plurality of feature vector datarepresentations based on the plurality of persona-based clusters enablesclassification of the plurality of online visitors into the plurality ofpersona-based dusters; and for each persona-based duster from among theplurality of persona-based dusters, training, by the processor, alearning model using utterances of online visitors classified into arespective persona-based duster, the learning model trained to mimic avisitor persona representative of the respective persona-based cluster,wherein the trained learning model is configured to facilitate thepersona-based agent interactions.
 2. The method of claim 1, furthercomprising performing, by the processor, for each interaction:extracting a plurality of utterances of the online visitor from thetextual transcript of the respective interaction; and for each utterancefrom among the plurality of utterances, performing a predefinedpersonality trait evaluation to facilitate extraction of the pluralityof persona related attributes from each interaction.
 3. The method ofclaim 1, wherein the predefined personality trait evaluation comprisesat least one of big-five personality factors based evaluation and thirtypersonality related facets based evaluation.
 4. The method of claim 1,wherein each persona-based cluster is associated with a cluster featurevector indicative of the visitor persona representative of therespective persona-based cluster, and wherein a clustering algorithm isconfigured to determine a similarity measure between the cluster featurevector of each persona-based cluster and each feature vector datarepresentation from among the plurality of feature vector datarepresentations to classify the plurality of feature vector datarepresentations into the plurality of persona-based clusters.
 5. Themethod of claim 1, wherein the learning model corresponds to a RecurrentNeural Network (RNN) based deep learning model.
 6. The method of claim1, wherein the trained learning model is configured to learn arepresentation of an utterance as a context and provide at least one ofa previous utterance and a subsequent utterance as a response.
 7. Themethod of claim 1, wherein the learning model trained to mimic thevisitor persona configures an automated conversational agentincorporating the visitor persona and, wherein the automatedconversational agent is used for facilitating training of live agents ininteracting with future online visitors associated with visitor personasubstantially matching the visitor persona incorporated in the automatedconversational agent.
 8. The method of claim 1, further comprising:extracting, by the processor, a plurality of agent persona relatedattributes from utterances of the agents engaged in the plurality ofinteractions; and training, by the processor, at least one automatedconversational agent to interact with future online visitors based onthe extracted plurality of agent persona related attributes.
 9. Themethod of claim 1, wherein the plurality of interactions comprises voiceinteractions and textual chat interactions between the agents and theplurality of online visitors of the enterprise.
 10. The method of claim9, further comprising: generating, by the processor, textualrepresentations of the voice interactions using at least one processingtechnique from among Natural Language Processing (NLP) and AutomaticSpeech Recognition (ASR).
 11. The method of claim 10, wherein eachtextual chat interaction and each textual representation of the voiceinteraction configures a textual transcript corresponding to therespective interaction.
 12. An apparatus for facilitating persona-basedagent interactions, the apparatus comprising: a memory for storinginstructions; and a processor configured to execute the instructions andthereby cause the apparatus to at least perform: extract a plurality ofpersona related attributes comprising characteristics reflectingbehavioral patterns, goals, motives, and personal values of an onlinevisitor from a textual transcript of each interaction from among aplurality of interactions between agents of an enterprise and aplurality of online visitors visiting enterprise interaction channels,the plurality of persona related attributes extracted from eachinteraction in relation to a persona of an online visitor engaged in therespective interaction; generate a feature vector data representationbased, at least in part, on the plurality of persona related attributesextracted from each interaction, wherein generating the feature vectordata representation in relation to each interaction from among theplurality of interactions configures a plurality of feature vector datarepresentations; classify the plurality of feature vector datarepresentations based on a plurality of persona-based clusters, whereinclassifying the plurality of feature vector data representations basedon the plurality of persona-based clusters enables classification of theplurality of online visitors into the plurality of persona-basedclusters; and for each persona-based cluster from among the plurality ofpersona-based clusters, train a learning model using utterances ofonline visitors classified into a respective persona-based cluster, thelearning model trained to mimic a visitor persona representative of therespective persona-based cluster, wherein the trained learning model isconfigured to facilitate the persona-based agent interactions.
 13. Theapparatus of claim 12, wherein the apparatus is further caused toperform for each interaction: extract a plurality of utterances of theonline visitor from the textual transcript of the respectiveinteraction; and for each utterance from among the plurality ofutterances, perform a predefined personality trait evaluation tofacilitate extraction of the plurality of persona related attributesfrom each interaction, wherein the predefined personality traitevaluation comprises at least one of big-five personality factors basedevaluation and thirty personality related facets based evaluation. 14.The apparatus of claim 12, wherein each persona-based cluster isassociated with a cluster feature vector indicative of the visitorpersona representative of the respective persona-based cluster, andwherein a clustering algorithm is configured to determine a similaritymeasure between the cluster feature vector of each persona-based clusterand each feature vector data representation from among the plurality offeature vector data representations to classify the plurality of featurevector data representations into the plurality of persona-basedclusters.
 15. The apparatus of claim 12, wherein the trained learningmodel is configured to learn a representation of an utterance as acontext and provide at least one of a previous utterance and asubsequent utterance as a response.
 16. The apparatus of claim 12,wherein the learning model is used for facilitating training of liveagents in interacting with future online visitors predicted to beassociated with visitor persona substantially matching the visitorpersona mimicked by the learning model.
 17. The apparatus of claim 12,wherein the learning model trained to mimic the visitor personaconfigures an automated conversational agent incorporating the visitorpersona and, wherein the automated conversational agent is used forfacilitating training of live agents in interacting with future onlinevisitors associated with visitor persona substantially matching thevisitor persona incorporated in the automated conversational agent. 18.A computer-implemented method for facilitating persona-based agentinteractions, the method comprising: performing, by a processor, foreach interaction from among a plurality of interactions between agentsof an enterprise and a plurality of online visitors visiting enterpriseinteraction channels: extract a plurality of utterances of an onlinevisitor from a textual transcript of a respective interaction, and foreach utterance from among the plurality of utterances, perform apredefined personality trait evaluation to extract a plurality ofpersona related attributes comprising characteristics reflectingbehavioral patterns goals, motives, and personal values of an onlinevisitor, wherein the plurality of persona related attributes areextracted from each interaction in relation to a persona of the onlinevisitor engaged in the respective interaction; generating, by theprocessor, a feature vector data representation based, at least in part,on the plurality of persona related attributes extracted from eachinteraction, wherein generating the feature vector data representationin relation to each interaction from among the plurality of interactionsconfigures a plurality of feature vector data representations;classifying, by the processor, the plurality of feature vector datarepresentations based on a plurality of persona-based clusters, whereinclassifying the plurality of feature vector data representations basedon the plurality of persona-based clusters enables classification of theplurality of online visitors into the plurality of persona-basedclusters; and for each persona-based cluster from among the plurality ofpersona-based clusters, training, by the processor, a Recurrent NeuralNetwork (RNN) model using utterances of online visitors classified intoa respective persona-based cluster, the RNN model trained to mimic avisitor persona representative of the respective persona-based cluster,wherein the trained learning model is configured to facilitate thepersona-based agent interactions.
 19. The method of claim 18, whereineach persona-based cluster is associated with a cluster feature vectorindicative of the visitor persona representative of the respectivepersona-based cluster, and wherein a clustering algorithm is configuredto determine a similarity measure between the cluster feature vector ofeach persona-based cluster and each feature vector data representationfrom among the plurality of feature vectors to classify the plurality offeature vectors into the plurality of persona-based clusters.
 20. Themethod of claim 18, wherein the RNN model trained to mimic the visitorpersona configures an automated conversational agent incorporating thevisitor persona and, wherein the automated conversational agent is usedfor facilitating training of live agents in interacting with futureonline visitors associated with visitor persona substantially matchingthe visitor persona incorporated in the automated conversational agent.